Overview

Dataset statistics

Number of variables9
Number of observations165166
Missing cells0
Missing cells (%)0.0%
Duplicate rows12
Duplicate rows (%)< 0.1%
Total size in memory12.6 MiB
Average record size in memory80.0 B

Variable types

Numeric9

Alerts

Dataset has 12 (< 0.1%) duplicate rowsDuplicates
voltage is highly overall correlated with current and 2 other fieldsHigh correlation
current is highly overall correlated with voltage and 2 other fieldsHigh correlation
workstation_cpu is highly overall correlated with voltage and 3 other fieldsHigh correlation
workstation_gpu is highly overall correlated with workstation_cpuHigh correlation
workstation_ram is highly overall correlated with voltage and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-07-25 11:19:17.426693
Analysis finished2023-07-25 11:19:22.933867
Duration5.51 seconds
Software versionydata-profiling vv4.3.2
Download configurationconfig.json

Variables

voltage
Real number (ℝ)

HIGH CORRELATION 

Distinct5855
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.7764128 × 10-14
Minimum-7.6853637
Maximum1.9911935
Zeros0
Zeros (%)0.0%
Negative74802
Negative (%)45.3%
Memory size2.5 MiB
2023-07-25T13:19:22.964695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-7.6853637
5-th percentile-1.029994
Q1-0.21202554
median0.037515511
Q30.42951689
95-th percentile1.8223073
Maximum1.9911935
Range9.6765572
Interquartile range (IQR)0.64154242

Descriptive statistics

Standard deviation1.000003
Coefficient of variation (CV)-5.6293393 × 1013
Kurtosis7.9926749
Mean-1.7764128 × 10-14
Median Absolute Deviation (MAD)0.32315896
Skewness-2.0783572
Sum-2.9214746 × 10-9
Variance1.0000061
MonotonicityNot monotonic
2023-07-25T13:19:23.011959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6338484122 4788
 
2.9%
1.881730224 3215
 
1.9%
0.2178878082 1739
 
1.1%
0.6264205443 865
 
0.5%
0.6189926763 741
 
0.4%
0.6265508577 719
 
0.4%
-0.1980727958 691
 
0.4%
0.6115648084 691
 
0.4%
0.009907506169 683
 
0.4%
0.6041369405 643
 
0.4%
Other values (5845) 150391
91.1%
ValueCountFrequency (%)
-7.685363668 3
< 0.1%
-7.458476066 1
 
< 0.1%
-7.420661466 1
 
< 0.1%
-7.401754165 1
 
< 0.1%
-7.373393215 3
< 0.1%
-7.335578615 3
< 0.1%
-7.327444079 1
 
< 0.1%
-7.316671315 1
 
< 0.1%
-7.307217665 1
 
< 0.1%
-7.297764014 1
 
< 0.1%
ValueCountFrequency (%)
1.991193541 1
 
< 0.1%
1.985720375 1
 
< 0.1%
1.947408214 1
 
< 0.1%
1.941153168 1
 
< 0.1%
1.94011066 1
 
< 0.1%
1.9337253 3
< 0.1%
1.932813105 1
 
< 0.1%
1.926297432 1
 
< 0.1%
1.925515551 2
 
< 0.1%
1.918869564 7
< 0.1%

current
Real number (ℝ)

HIGH CORRELATION 

Distinct20278
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.204007 × 10-14
Minimum-3.2341542
Maximum3.286062
Zeros0
Zeros (%)0.0%
Negative68868
Negative (%)41.7%
Memory size2.5 MiB
2023-07-25T13:19:23.186100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3.2341542
5-th percentile-2.7773237
Q1-0.71393653
median0.50469238
Q30.60218319
95-th percentile1.0459057
Maximum3.286062
Range6.5202161
Interquartile range (IQR)1.3161197

Descriptive statistics

Standard deviation1.000003
Coefficient of variation (CV)8.3056248 × 1013
Kurtosis1.5638647
Mean1.204007 × 10-14
Median Absolute Deviation (MAD)0.49066007
Skewness-1.2679018
Sum1.9226017 × 10-9
Variance1.0000061
MonotonicityNot monotonic
2023-07-25T13:19:23.232119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.777323743 11336
 
6.9%
-0.7371605698 544
 
0.3%
-0.736418962 486
 
0.3%
-0.7379021777 477
 
0.3%
-0.7356773541 415
 
0.3%
-0.7365230473 410
 
0.2%
-0.7372516445 396
 
0.2%
-0.7379802417 396
 
0.2%
-0.7386437855 346
 
0.2%
-0.7357944501 340
 
0.2%
Other values (20268) 150020
90.8%
ValueCountFrequency (%)
-3.234154174 106
 
0.1%
-2.789931077 1
 
< 0.1%
-2.787706253 1
 
< 0.1%
-2.785481429 1
 
< 0.1%
-2.783256606 1
 
< 0.1%
-2.778806959 1
 
< 0.1%
-2.777323743 11336
6.9%
-2.487355077 1
 
< 0.1%
-2.236691626 1
 
< 0.1%
-2.138057783 1
 
< 0.1%
ValueCountFrequency (%)
3.286061973 1
< 0.1%
2.660886562 1
< 0.1%
2.587337279 1
< 0.1%
2.585880085 1
< 0.1%
2.462877269 1
< 0.1%
2.414659748 1
< 0.1%
2.222596328 1
< 0.1%
2.18952322 1
< 0.1%
2.170683779 1
< 0.1%
2.132861779 1
< 0.1%

frequency
Real number (ℝ)

Distinct3313
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3865277 × 10-16
Minimum-7.5178823
Maximum3.6914658
Zeros0
Zeros (%)0.0%
Negative77062
Negative (%)46.7%
Memory size2.5 MiB
2023-07-25T13:19:23.282387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-7.5178823
5-th percentile-1.7561691
Q1-0.65344883
median0.10791464
Q30.76728806
95-th percentile1.4199554
Maximum3.6914658
Range11.209348
Interquartile range (IQR)1.4207369

Descriptive statistics

Standard deviation1.000003
Coefficient of variation (CV)2.952886 × 1015
Kurtosis0.69872949
Mean3.3865277 × 10-16
Median Absolute Deviation (MAD)0.69922071
Skewness-0.67131828
Sum6.6131864 × 10-11
Variance1.0000061
MonotonicityNot monotonic
2023-07-25T13:19:23.330687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.579559666 3857
 
2.3%
0.4423794155 2369
 
1.4%
0.279925094 2365
 
1.4%
0.5236065762 2348
 
1.4%
0.3611522547 2309
 
1.4%
0.1174707725 2287
 
1.4%
0.1986979333 2255
 
1.4%
-0.1262107097 2253
 
1.4%
0.6048337369 2251
 
1.4%
0.6860608977 2212
 
1.3%
Other values (3303) 140660
85.2%
ValueCountFrequency (%)
-7.517882336 1
< 0.1%
-7.517882336 2
< 0.1%
-7.517882336 1
< 0.1%
-7.467896391 1
< 0.1%
-7.374238515 1
< 0.1%
-7.355428015 1
< 0.1%
-7.290446286 1
< 0.1%
-7.274200854 1
< 0.1%
-7.146558173 1
< 0.1%
-7.030519372 1
< 0.1%
ValueCountFrequency (%)
3.691465845 2
< 0.1%
3.610238684 1
< 0.1%
3.557264449 1
< 0.1%
3.529011523 1
< 0.1%
3.492573171 1
< 0.1%
3.045676352 1
< 0.1%
2.78718471 1
< 0.1%
2.739037568 1
< 0.1%
2.53718514 1
< 0.1%
2.473058434 1
< 0.1%

energy
Real number (ℝ)

Distinct56169
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.0858061 × 10-15
Minimum-1.6889616
Maximum3.7069541
Zeros0
Zeros (%)0.0%
Negative77880
Negative (%)47.2%
Memory size2.5 MiB
2023-07-25T13:19:23.377052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.6889616
5-th percentile-1.5169929
Q1-0.82050954
median0.016253571
Q30.66088679
95-th percentile1.6206964
Maximum3.7069541
Range5.3959157
Interquartile range (IQR)1.4813963

Descriptive statistics

Standard deviation1.000003
Coefficient of variation (CV)-1.1006211 × 1014
Kurtosis0.31323276
Mean-9.0858061 × 10-15
Median Absolute Deviation (MAD)0.74631235
Skewness0.46603792
Sum-1.511792 × 10-9
Variance1.0000061
MonotonicityNot monotonic
2023-07-25T13:19:23.423393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02561054666 136
 
0.1%
0.01413265698 136
 
0.1%
0.01625357138 136
 
0.1%
0.01762592775 136
 
0.1%
0.01800020676 136
 
0.1%
0.01587929237 136
 
0.1%
0.02324011292 136
 
0.1%
0.01837448577 136
 
0.1%
0.01550501336 136
 
0.1%
0.01525549401 136
 
0.1%
Other values (56159) 163806
99.2%
ValueCountFrequency (%)
-1.688961603 2
 
< 0.1%
-1.688712084 6
< 0.1%
-1.6886099 1
 
< 0.1%
-1.688587324 5
< 0.1%
-1.68850729 1
 
< 0.1%
-1.688462565 5
< 0.1%
-1.688403854 1
 
< 0.1%
-1.688337805 6
< 0.1%
-1.688256269 1
 
< 0.1%
-1.688213045 5
< 0.1%
ValueCountFrequency (%)
3.706954094 1
< 0.1%
3.706935727 1
< 0.1%
3.706912231 1
< 0.1%
3.706888734 1
< 0.1%
3.706866901 1
< 0.1%
3.706844029 1
< 0.1%
3.706821156 1
< 0.1%
3.706799323 1
< 0.1%
3.706776035 1
< 0.1%
3.706753578 1
< 0.1%

power_factor
Real number (ℝ)

Distinct7013
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.5616147 × 10-15
Minimum-5.6805812
Maximum0.74723965
Zeros0
Zeros (%)0.0%
Negative20367
Negative (%)12.3%
Memory size2.5 MiB
2023-07-25T13:19:23.469852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-5.6805812
5-th percentile-3.5604285
Q10.021613243
median0.25442823
Q30.34884799
95-th percentile0.67751333
Maximum0.74723965
Range6.4278208
Interquartile range (IQR)0.32723474

Descriptive statistics

Standard deviation1.000003
Coefficient of variation (CV)-1.7980444 × 1014
Kurtosis8.7496339
Mean-5.5616147 × 10-15
Median Absolute Deviation (MAD)0.2213909
Skewness-3.1657734
Sum-9.41626 × 10-10
Variance1.0000061
MonotonicityNot monotonic
2023-07-25T13:19:23.515690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.562792095 2836
 
1.7%
-3.562792095 2274
 
1.4%
0.2757006642 1297
 
0.8%
0.01097702567 1211
 
0.7%
0.2709734564 1016
 
0.6%
0.2697916544 1012
 
0.6%
0.2733370603 1004
 
0.6%
0.2721552584 969
 
0.6%
0.2686098525 968
 
0.6%
0.2662462486 900
 
0.5%
Other values (7003) 151679
91.8%
ValueCountFrequency (%)
-5.680581203 106
0.1%
-4.48932483 1
 
< 0.1%
-3.699881122 1
 
< 0.1%
-3.686881301 1
 
< 0.1%
-3.664427063 1
 
< 0.1%
-3.597624153 2
 
< 0.1%
-3.595302015 1
 
< 0.1%
-3.594700748 2
 
< 0.1%
-3.594140947 2
 
< 0.1%
-3.593518946 1
 
< 0.1%
ValueCountFrequency (%)
0.7472396454 1
 
< 0.1%
0.7459334433 3
 
< 0.1%
0.7448760415 3
 
< 0.1%
0.7447723747 5
 
< 0.1%
0.7436942396 3
 
< 0.1%
0.7436113061 6
 
< 0.1%
0.7425124376 15
< 0.1%
0.7424502375 11
< 0.1%
0.7413306356 21
< 0.1%
0.7412891689 13
< 0.1%

esp32_temperature
Real number (ℝ)

Distinct14063
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.8380648 × 10-14
Minimum-2.0455106
Maximum1.9767819
Zeros0
Zeros (%)0.0%
Negative53806
Negative (%)32.6%
Memory size2.5 MiB
2023-07-25T13:19:23.564007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2.0455106
5-th percentile-2.0455106
Q1-0.043916063
median0.43113673
Q30.50889811
95-th percentile0.59189764
Maximum1.9767819
Range4.0222925
Interquartile range (IQR)0.55281417

Descriptive statistics

Standard deviation1.000003
Coefficient of variation (CV)-2.6054876 × 1013
Kurtosis0.54051986
Mean-3.8380648 × 10-14
Median Absolute Deviation (MAD)0.11858139
Skewness-1.0841636
Sum-6.4287002 × 10-9
Variance1.0000061
MonotonicityNot monotonic
2023-07-25T13:19:23.609456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.045510618 27910
 
16.9%
1.976781881 659
 
0.4%
0.5096522886 475
 
0.3%
0.5126286073 451
 
0.3%
0.5133693201 416
 
0.3%
0.5141100329 415
 
0.3%
0.5111337142 399
 
0.2%
0.511874427 388
 
0.2%
0.5118878945 371
 
0.2%
0.5096388211 350
 
0.2%
Other values (14053) 133332
80.7%
ValueCountFrequency (%)
-2.045510618 27910
16.9%
-0.3723287993 1
 
< 0.1%
-0.3327805907 1
 
< 0.1%
-0.3326745046 1
 
< 0.1%
-0.3320264104 1
 
< 0.1%
-0.3312722301 1
 
< 0.1%
-0.3312058377 1
 
< 0.1%
-0.3305180498 3
 
< 0.1%
-0.3304911148 1
 
< 0.1%
-0.3304648886 1
 
< 0.1%
ValueCountFrequency (%)
1.976781881 659
0.4%
1.976781881 2
 
< 0.1%
1.970166264 1
 
< 0.1%
1.96491058 1
 
< 0.1%
1.953805169 1
 
< 0.1%
1.952636862 1
 
< 0.1%
1.952054657 1
 
< 0.1%
1.951926654 1
 
< 0.1%
1.951642537 1
 
< 0.1%
1.950944292 1
 
< 0.1%

workstation_cpu
Real number (ℝ)

HIGH CORRELATION 

Distinct33858
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.0232332 × 10-13
Minimum-0.65920355
Maximum14.281485
Zeros0
Zeros (%)0.0%
Negative110850
Negative (%)67.1%
Memory size2.5 MiB
2023-07-25T13:19:23.656927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.65920355
5-th percentile-0.65920355
Q1-0.65920355
median-0.65920355
Q30.79191514
95-th percentile2.1129301
Maximum14.281485
Range14.940689
Interquartile range (IQR)1.4511187

Descriptive statistics

Standard deviation1.000003
Coefficient of variation (CV)-3.3077271 × 1012
Kurtosis2.5360367
Mean-3.0232332 × 10-13
Median Absolute Deviation (MAD)0
Skewness1.5655673
Sum-5.0274377 × 10-8
Variance1.0000061
MonotonicityNot monotonic
2023-07-25T13:19:23.701127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.659203547 92498
56.0%
-0.4756140431 82
 
< 0.1%
-0.5564808484 63
 
< 0.1%
-0.4652324938 62
 
< 0.1%
-0.5537488617 62
 
< 0.1%
-0.5542952591 61
 
< 0.1%
-0.4570365338 60
 
< 0.1%
-0.4679644804 59
 
< 0.1%
-0.4685108778 59
 
< 0.1%
-0.4564901364 58
 
< 0.1%
Other values (33848) 72102
43.7%
ValueCountFrequency (%)
-0.659203547 92498
56.0%
-0.6286052964 1
 
< 0.1%
-0.6111205817 1
 
< 0.1%
-0.606749403 1
 
< 0.1%
-0.6031067541 1
 
< 0.1%
-0.6023782244 2
 
< 0.1%
-0.6020868125 1
 
< 0.1%
-0.5999194364 1
 
< 0.1%
-0.5998069428 1
 
< 0.1%
-0.5980070457 3
 
< 0.1%
ValueCountFrequency (%)
14.28148513 1
< 0.1%
9.641133883 1
< 0.1%
7.890931201 1
< 0.1%
7.425400673 1
< 0.1%
7.283774484 1
< 0.1%
7.153294801 1
< 0.1%
7.072209437 1
< 0.1%
7.069753525 1
< 0.1%
6.677559105 1
< 0.1%
6.484504465 1
< 0.1%

workstation_gpu
Real number (ℝ)

HIGH CORRELATION 

Distinct1201
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3358141 × 10-14
Minimum-0.190153
Maximum36.982539
Zeros0
Zeros (%)0.0%
Negative151783
Negative (%)91.9%
Memory size2.5 MiB
2023-07-25T13:19:23.747673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.190153
5-th percentile-0.190153
Q1-0.190153
median-0.190153
Q3-0.190153
95-th percentile1.242025
Maximum36.982539
Range37.172692
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.000003
Coefficient of variation (CV)1.1996465 × 1013
Kurtosis189.20629
Mean8.3358141 × 10-14
Median Absolute Deviation (MAD)0
Skewness11.23747
Sum1.376228 × 10-8
Variance1.0000061
MonotonicityNot monotonic
2023-07-25T13:19:23.791655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1901530003 142018
86.0%
-0.08602781259 4013
 
2.4%
-0.08785457027 2189
 
1.3%
0.01809737508 1255
 
0.8%
0.01444385972 722
 
0.4%
-0.1859879928 511
 
0.3%
-0.1860610631 424
 
0.3%
1.267599627 346
 
0.2%
1.684100378 337
 
0.2%
-0.141561246 298
 
0.2%
Other values (1191) 13053
 
7.9%
ValueCountFrequency (%)
-0.1901530003 142018
86.0%
-0.189130016 29
 
< 0.1%
-0.1891117484 39
 
< 0.1%
-0.1882093301 11
 
< 0.1%
-0.1881070317 27
 
< 0.1%
-0.1880704965 27
 
< 0.1%
-0.1877234125 1
 
< 0.1%
-0.1876177783 2
 
< 0.1%
-0.1873763286 1
 
< 0.1%
-0.1870840474 23
 
< 0.1%
ValueCountFrequency (%)
36.982539 1
< 0.1%
32.14545073 1
< 0.1%
32.01664337 1
< 0.1%
30.55517514 1
< 0.1%
30.27390191 1
< 0.1%
29.69377586 1
< 0.1%
26.97263762 1
< 0.1%
26.63249534 1
< 0.1%
26.61203566 1
< 0.1%
26.29235306 1
< 0.1%

workstation_ram
Real number (ℝ)

HIGH CORRELATION 

Distinct41043
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5654135 × 10-14
Minimum-0.87137353
Maximum2.0369436
Zeros0
Zeros (%)0.0%
Negative92517
Negative (%)56.0%
Memory size2.5 MiB
2023-07-25T13:19:23.840524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.87137353
5-th percentile-0.87137353
Q1-0.87137353
median-0.87137353
Q31.1029211
95-th percentile1.464132
Maximum2.0369436
Range2.9083172
Interquartile range (IQR)1.9742947

Descriptive statistics

Standard deviation1.000003
Coefficient of variation (CV)1.3218088 × 1013
Kurtosis-1.7662416
Mean7.5654135 × 10-14
Median Absolute Deviation (MAD)0
Skewness0.33886863
Sum1.2564411 × 10-8
Variance1.0000061
MonotonicityNot monotonic
2023-07-25T13:19:23.884114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8713735334 92498
56.0%
0.664446415 240
 
0.1%
0.6220789681 236
 
0.1%
0.5797115213 209
 
0.1%
0.5055684893 192
 
0.1%
0.627374899 191
 
0.1%
0.5691196596 191
 
0.1%
0.5214562819 187
 
0.1%
0.6485586224 180
 
0.1%
0.6061911756 170
 
0.1%
Other values (41033) 70872
42.9%
ValueCountFrequency (%)
-0.8713735334 92498
56.0%
-0.4282806517 1
 
< 0.1%
-0.3617725867 1
 
< 0.1%
-0.3402975871 1
 
< 0.1%
-0.3398739126 1
 
< 0.1%
-0.3394502382 1
 
< 0.1%
-0.3159293759 1
 
< 0.1%
-0.31565147 1
 
< 0.1%
-0.3018015738 1
 
< 0.1%
-0.2879605504 1
 
< 0.1%
ValueCountFrequency (%)
2.036943639 1
 
< 0.1%
2.004411492 1
 
< 0.1%
1.996512392 1
 
< 0.1%
1.977931838 1
 
< 0.1%
1.967059584 1
 
< 0.1%
1.962422326 1
 
< 0.1%
1.953494568 1
 
< 0.1%
1.947176615 1
 
< 0.1%
1.946439963 1
 
< 0.1%
1.946061682 3
< 0.1%

Interactions

2023-07-25T13:19:22.222984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:18.274009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:18.841832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:19.540204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:19.996280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:20.396356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:20.935039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.371152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.799235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:22.267104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:18.328847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:18.884571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:19.591691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:20.039731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:20.440964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:20.982132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.416511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.845275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:22.313262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:18.381068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:18.930289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:19.653794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:20.082568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:20.486509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.029058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.464224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.894327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:22.359384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:18.430149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:18.975439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:19.707568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:20.124513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:20.533053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.077676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.511985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.941876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:22.402735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:18.581781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:19.019331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:19.751680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:20.166211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:20.575923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.125412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.556137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.986007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:22.451203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:18.629839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:19.065583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:19.799550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:20.211633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:20.622853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.174743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.605163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:22.034483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:22.501221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:18.683067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:19.113721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:19.850348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:20.259513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:20.672729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.224199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.654206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:22.084181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:22.549071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:18.743686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:19.200358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:19.899006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:20.305785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:20.832435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.272007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.701537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:22.129807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:22.595001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:18.795703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:19.430910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:19.948597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:20.351803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:20.881528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.323490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:21.750373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:19:22.176835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-25T13:19:23.922973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
voltagecurrentfrequencyenergypower_factoresp32_temperatureworkstation_cpuworkstation_gpuworkstation_ram
voltage1.000-0.9250.132-0.145-0.1450.282-0.690-0.326-0.640
current-0.9251.000-0.0030.0510.012-0.3390.6510.2600.666
frequency0.132-0.0031.000-0.0040.0180.0330.0070.0000.015
energy-0.1450.051-0.0041.0000.4700.2230.4280.1750.354
power_factor-0.1450.0120.0180.4701.0000.2170.4100.2920.215
esp32_temperature0.282-0.3390.0330.2230.2171.0000.0050.134-0.051
workstation_cpu-0.6900.6510.0070.4280.4100.0051.0000.5460.877
workstation_gpu-0.3260.2600.0000.1750.2920.1340.5461.0000.360
workstation_ram-0.6400.6660.0150.3540.215-0.0510.8770.3601.000

Missing values

2023-07-25T13:19:22.651985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-25T13:19:22.754853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

voltagecurrentfrequencyenergypower_factoresp32_temperatureworkstation_cpuworkstation_gpuworkstation_ram
fecha_servidor
2021-05-05 22:05:00-0.2121730.7323170.885687-1.6889620.122026-2.045511-0.659204-0.190153-0.871374
2021-05-05 22:06:00-0.1257320.6155001.184019-1.6889620.048384-2.045511-0.659204-0.190153-0.871374
2021-05-05 22:14:00-0.2535340.9659171.579560-1.6887120.249228-2.045511-0.659204-0.190153-0.871374
2021-05-05 22:15:00-0.0524870.6497351.397611-1.6887120.072525-2.045511-0.659204-0.190153-0.871374
2021-05-05 22:16:00-0.0543770.5971810.049535-1.6887120.030230-2.045511-0.659204-0.190153-0.871374
2021-05-05 22:17:00-0.0093500.5973760.652968-1.6887120.031199-2.045511-0.659204-0.190153-0.871374
2021-05-05 22:18:00-0.0406820.595963-0.059619-1.6887120.028268-2.045511-0.659204-0.190153-0.871374
2021-05-05 22:19:000.0118510.5811760.899377-1.6887120.015925-2.045511-0.659204-0.190153-0.871374
2021-05-05 22:20:000.0911190.5743480.496531-1.6886100.009086-2.045511-0.659204-0.190153-0.871374
2021-05-05 22:21:00-0.0066570.577789-0.513657-1.6885870.013320-2.045511-0.659204-0.190153-0.871374
voltagecurrentfrequencyenergypower_factoresp32_temperatureworkstation_cpuworkstation_gpuworkstation_ram
fecha_servidor
2021-12-04 08:09:00-3.5257580.559117-2.5932341.2484960.5502691.7762912.272008-0.1901531.077131
2021-12-04 08:10:00-3.5500220.621633-0.0883051.2269810.5591761.8430552.544943-0.1901531.073614
2021-12-04 08:11:00-3.5361570.5667790.5181911.2270030.5498001.7742712.310229-0.1901531.077551
2021-12-04 08:12:00-3.5361570.590070-2.1731351.2270240.5602791.7424982.465235-0.1901531.079586
2021-12-04 08:13:00-3.5257580.566225-1.1875791.2270460.5558671.8011562.228965-0.1901531.080230
2021-12-04 08:14:00-3.5673540.563180-0.0124931.2270680.5509031.7857932.300871-0.1901531.079780
2021-12-04 08:15:00-3.4980270.542415-2.0594171.2270890.5486971.7980142.291692-0.1901531.081638
2021-12-04 08:16:00-3.5921720.5211731.4266611.2052110.5509911.7641192.120069-0.1901531.070529
2021-12-04 08:17:00-3.5257580.525110-0.6948011.2271330.5492481.8290892.131586-0.1901531.075331
2021-12-04 08:18:00-3.5417560.533579-3.1441121.2271460.5506061.9767822.005904-0.1901531.075635

Duplicate rows

Most frequently occurring

voltagecurrentfrequencyenergypower_factoresp32_temperatureworkstation_cpuworkstation_gpuworkstation_ram# duplicates
0-0.1980730.5866091.579560-1.4818610.010977-2.045511-0.659204-0.190153-0.8713742
11.866874-2.7773240.5236070.015879-3.5627920.551913-0.659204-0.190153-0.8713742
21.874302-2.7773240.4423790.020620-3.5627920.545974-0.659204-0.190153-0.8713742
31.874302-2.7773240.8485150.018749-3.5627920.526702-0.659204-0.190153-0.8713742
41.881730-2.7773240.1986980.015879-3.5627920.556411-0.659204-0.190153-0.8713742
51.881730-2.7773240.9297420.021743-3.5627920.518554-0.659204-0.190153-0.8713742
61.881730-2.7773240.9411430.021868-3.5627920.516956-0.659204-0.190153-0.8713742
71.881730-2.7773241.0109700.016004-3.5627920.553408-0.659204-0.190153-0.8713742
81.881730-2.7773241.1734240.020495-3.5627920.550418-0.659204-0.190153-0.8713742
91.881730-2.7773241.4199550.013010-3.5627920.519139-0.659204-0.190153-0.8713742